

from typing import Any

import numpy as np

def       __snake_case						( __UpperCamelCase	: np.ndarray    ):



      """simple docstring"""

      return np.array_equal(__UpperCamelCase				,matrix.conjugate().T    )

def       __snake_case						( __UpperCamelCase	: np.ndarray				,__UpperCamelCase	: np.ndarray    ):



      """simple docstring"""

      A_						  =				v.conjugate().T
      A_						  =				v_star.dot(__UpperCamelCase    )
      assert isinstance(__UpperCamelCase				,np.ndarray    )
      return (v_star_dot.dot(__UpperCamelCase    )) / (v_star.dot(__UpperCamelCase    ))

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]    )
      A_						  =				np.array([[1], [2], [3]]    )
      assert is_hermitian(__UpperCamelCase    ), f'''{a} is not hermitian.'''
      print(rayleigh_quotient(__UpperCamelCase				,__UpperCamelCase    )    )

      A_						  =				np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]    )
      assert is_hermitian(__UpperCamelCase    ), f'''{a} is not hermitian.'''
      assert rayleigh_quotient(__UpperCamelCase				,__UpperCamelCase    ) == float(3    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      tests()


import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


lowercase			:				Optional[int]   =     logging.getLogger(__name__)

lowercase			:				str   =     "pytorch_model.bin"







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}		,       )







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}				)
							UpperCAmelCase_						:		str								=	dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''A csv or a json file containing the validation data.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The name of the task to train on.'''}		,       )
							UpperCAmelCase_						:		Optional[List[str]]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The list of labels for the task.'''}				)







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(
							    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default='''accuracy'''		,       metadata={'''help''': '''The evaluation metric used for the task.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default='''no'''		,       metadata={
							        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
							    }		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=10		,       metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,       )
							UpperCAmelCase_						:		Optional[float]								=	dataclasses.field(
							    default=0.0		,       metadata={
							        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
							    }		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}		,       )
							UpperCAmelCase_						:		Optional[float]								=	dataclasses.field(
							    default=0.0		,       metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=1_00		,       metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Random seed for initialization.'''}		,       )






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					datasets.concatenate_datasets([infer_input, infer_output]     ,				axis=1		)

							if args.do_filter_by_confidence:
														A      :					Union[str, Any]      		=					dataset.filter(lambda lowerCamelCase_		: example["probability"] > args.confidence_threshold		)

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														A      :					Optional[int]      		=					int(eval_result * len(lowerCamelCase_		)		)
														print(lowerCamelCase_		)
														A      :					Any      		=					dataset.sort('''probability'''     ,				reverse=lowerCamelCase_		)
														A      :					Any      		=					dataset.select(range(lowerCamelCase_		)		)

							A      :					Dict      		=					dataset.remove_columns(['''label''', '''probability''']		)
							A      :					List[str]      		=					dataset.rename_column('''prediction'''     ,				'''label'''		)
							A      :					Tuple      		=					dataset.map(lambda lowerCamelCase_		: {"label": idalabel[example["label"]]}		)
							A      :					Dict      		=					dataset.shuffle(seed=args.seed		)

							A      :					str      		=					os.path.join(lowerCamelCase_     ,				F'train_pseudo.{args.data_file_extension}'		)
							if args.data_file_extension == "csv":
														dataset.to_csv(lowerCamelCase_     ,				index=lowerCamelCase_		)
							else:
														dataset.to_json(lowerCamelCase_		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				**lowerCamelCase_		):
							A      :					Optional[Any]      		=					Accelerator()
							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,				datefmt='''%m/%d/%Y %H:%M:%S'''     ,				level=logging.INFO     ,				)
							logger.info(accelerator.state		)

							# Setup logging, we only want one process per machine to log things on the
							# screen. accelerator.is_local_main_process is only True for one process per
							# machine.
							logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_info()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()

							A      :					List[str]      		=					STModelArguments(model_name_or_path=lowerCamelCase_		)
							A      :					List[Any]      		=					STDataArguments(train_file=lowerCamelCase_     ,				infer_file=lowerCamelCase_		)
							A      :					int      		=					STTrainingArguments(output_dir=lowerCamelCase_		)
							A      :					Any      		=					argparse.Namespace()

							for arg_class in (model_args, data_args, training_args):
														for key, value in vars(lowerCamelCase_		).items():
																					setattr(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)

							for key, value in kwargs.items():
														if hasattr(lowerCamelCase_     ,				lowerCamelCase_		):
																					setattr(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)

    # Sanity checks
							A      :					Tuple      		=					{}
							A      :					str      		=					None

							# You need to provide the training data and the data to predict on
							assert args.train_file is not None
							assert args.infer_file is not None
							A      :					Dict      		=					args.train_file
							A      :					Any      		=					args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														A      :					Optional[int]      		=					args.eval_file

							for key in data_files:
														A      :					str      		=					data_files[key].split('''.'''		)[-1]
														assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
														if args.data_file_extension is None:
																					A      :					List[Any]      		=					extension
														else:
																					assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'

							assert (
							    args.eval_metric in datasets.list_metrics()
							), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'

							# If passed along, set the training seed now.
							if args.seed is not None:
														set_seed(args.seed		)

							logger.info('''Creating the initial data directory for self-training...'''		)
							A      :					Any      		=					F'{args.output_dir}/self-train_iter-{{}}'.format
							A      :					Tuple      		=					data_dir_format(0		)

							if accelerator.is_main_process:
														if args.output_dir is not None:
																					os.makedirs(args.output_dir     ,				exist_ok=lowerCamelCase_		)
																					os.makedirs(lowerCamelCase_     ,				exist_ok=lowerCamelCase_		)
							accelerator.wait_for_everyone()

							A      :					str      		=					None
							A      :					List[str]      		=					None
							A      :					List[str]      		=					0
							A      :					List[Any]      		=					False
							# Show the progress bar
							A      :					Any      		=					tqdm(range(args.max_selftrain_iterations		)     ,				disable=not accelerator.is_local_main_process		)

							# Self-train
							for iteration in range(0     ,				int(args.max_selftrain_iterations		)		):
														A      :					Union[str, Any]      		=					data_dir_format(lowerCamelCase_		)
														assert os.path.exists(lowerCamelCase_		)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														A      :					str      		=					os.path.join(lowerCamelCase_     ,				'''stage-1'''		)
														A      :					Optional[Any]      		=					{
														    '''accelerator''': accelerator,
														    '''model_name_or_path''': args.model_name_or_path,
														    '''cache_dir''': args.cache_dir,
														    '''do_train''': True,
														    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
														    '''do_eval''': True if args.eval_file is not None else False,
														    '''eval_file''': data_files['''eval'''],
														    '''do_predict''': True,
														    '''infer_file''': data_files['''infer'''],
														    '''task_name''': args.task_name,
														    '''label_list''': args.label_list,
														    '''output_dir''': current_output_dir,
														    '''eval_metric''': args.eval_metric,
														    '''evaluation_strategy''': args.evaluation_strategy,
														    '''early_stopping_patience''': args.early_stopping_patience,
														    '''early_stopping_threshold''': args.early_stopping_threshold,
														    '''seed''': args.seed,
														}
														# Add additional training arguments
														for key, value in kwargs.items():
																					if key not in arguments_dict and not hasattr(lowerCamelCase_     ,				lowerCamelCase_		):
																												arguments_dict.update({key: value}		)

														A      :					str      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''     ,				lowerCamelCase_		)
														if os.path.exists(lowerCamelCase_		):
																					logger.info(
																					    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				)
														else:
																					logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''     ,				lowerCamelCase_		)
																					finetune(**lowerCamelCase_		)
																					accelerator.wait_for_everyone()
																					assert os.path.exists(lowerCamelCase_		)
																					logger.info('''Self-training job completed: iteration: %d, stage: 1.'''     ,				lowerCamelCase_		)

														if iteration > 0 and args.finetune_on_labeled_data:
																					# Stage 2 (optional): fine-tuning on the original labeled data
																					A      :					Any      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''		)
																					A      :					int      		=					os.path.join(lowerCamelCase_     ,				'''stage-2'''		)
																					# Update arguments_dict
																					A      :					int      		=					model_path
																					A      :					Optional[Any]      		=					data_files['''train''']
																					A      :					Tuple      		=					current_output_dir

																					A      :					List[str]      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''     ,				lowerCamelCase_		)
																					if os.path.exists(lowerCamelCase_		):
																												logger.info(
																												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				)
																					else:
																												logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''     ,				lowerCamelCase_		)
																												finetune(**lowerCamelCase_		)
																												accelerator.wait_for_everyone()
																												assert os.path.exists(lowerCamelCase_		)
																												logger.info('''Self-training job completed: iteration: %d, stage: 2.'''     ,				lowerCamelCase_		)

														A      :					List[Any]      		=					iteration
														A      :					Optional[Any]      		=					data_dir_format(iteration + 1		)

														A      :					Tuple      		=					AutoConfig.from_pretrained(os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''		)		)
														A      :					List[Any]      		=					config.idalabel
														A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				'''eval_results_best-checkpoint.json'''		)
														A      :					Union[str, Any]      		=					os.path.join(lowerCamelCase_     ,				'''test_results_best-checkpoint.json'''		)
														assert os.path.exists(lowerCamelCase_		)

														with open(lowerCamelCase_     ,				'''r'''		) as f:
																					A      :					Optional[int]      		=					float(json.load(lowerCamelCase_		)[args.eval_metric]		)
														A      :					Any      		=					os.path.join(lowerCamelCase_     ,				'''infer_output_best-checkpoint.csv'''		)
														assert os.path.exists(lowerCamelCase_		)
														# Loading the dataset from local csv or json files.
														A      :					str      		=					load_dataset(args.data_file_extension     ,				data_files={'''data''': data_files['''infer''']}		)['''data''']
														A      :					Tuple      		=					load_dataset('''csv'''     ,				data_files={'''data''': infer_output_file}		)['''data''']

														if accelerator.is_main_process:
																					os.makedirs(lowerCamelCase_     ,				exist_ok=lowerCamelCase_		)
																					shutil.copy(lowerCamelCase_     ,				os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{iteration}.json'		)		)
																					if os.path.exists(lowerCamelCase_		):
																												shutil.copy(lowerCamelCase_     ,				os.path.join(lowerCamelCase_     ,				F'test_results_iter-{iteration}.json'		)		)
																					create_pseudo_labeled_data(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()

														A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				F'train_pseudo.{args.data_file_extension}'		)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					A      :					Any      		=					eval_result

																					if best_iteration is None:
																												A      :					int      		=					new_iteration
																												A      :					Optional[Any]      		=					new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			A      :					Optional[int]      		=					new_iteration
																																			A      :					Any      		=					new_eval_result
																																			A      :					Any      		=					0
																												else:
																																			if new_eval_result == best_eval_result:
																																										A      :					Any      		=					new_iteration
																																										A      :					Any      		=					new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			A      :					int      		=					True

														progress_bar.update(1		)

														if should_training_stop:
																					break

							if best_iteration is not None:
														# Save the best iteration
														logger.info('''Best iteration: %d'''     ,				lowerCamelCase_		)
														logger.info('''Best evaluation result: %s = %f'''     ,				args.eval_metric     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{iteration}.json'		)     ,				os.path.join(lowerCamelCase_     ,				'''eval_results_best-iteration.json'''		)     ,				)
							else:
														# Assume that the last iteration is the best
														logger.info('''Best iteration: %d'''     ,				args.max_selftrain_iterations - 1		)
														logger.info('''Best evaluation result: %s = %f'''     ,				args.eval_metric     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{args.max_selftrain_iterations - 1}.json'		)     ,				os.path.join(lowerCamelCase_     ,				'''eval_results_best-iteration.json'''		)     ,				)






